With the advent of the Human Genome Project, the International HapMap Project, and high-throughput yet cost-effective genotyping, it is now possible to interrogate the human genome with >1 million markers for associations with common human diseases and traits. Within the last few months, the literature has become inundated with reports of genome-wide association (GWA) studies identifying new genetic variations associated with phenotypes of public health interest. While the flurry of discovery is exciting, the usefulness of these new findings in a general population setting remains unclear. Therefore, the next steps beyond the initial GWA studies must provide population-based data on these initial associations before the most promising findings can be translated into improvements in intervention, prevention, and/or treatment options for the general population. The genetic component of the National Health and Nutrition Examination Surveys (NHANES;n~20,000) can provide the data necessary to move beyond the initial GWA study discoveries. NHANES is a U.S. population-based, cross-sectional survey collected by the Centers for Disease Control and Prevention. NHANES DNAs are linked to demographic, health, lifestyle, laboratory, extensive clinical, and physical examination data for participating individuals. Because participants are ascertained regardless of health status, NHANES is a rich resource for phenotypes (clinical endpoints and quantitative traits) and environmental exposures. With this large dataset, the epidemiologic architecture of GWA-identified genetic variations will be described, and association studies will be conducted to provide more accurate effect size estimates and population attributable fractions for many common diseases and traits such as type 2 diabetes, obesity, and coronary artery disease. Finally, tests for gene-environment and nuclear mitochondrial gene interactions will be performed, the latter of which laboratory data will be generated to support the statistical association. The purpose of this grant is to provide evidence in population-based datasets that GWA-identified genetic variations are relevant to most people. As such, GWA-identified variations will be characterized and population-based statistics and data for modifiers of these variations will be released rapidly so that the most promising findings can be incorporated into future translational studies by the research community.